Title :
Discovering multivariate linear relationship securely
Author :
Wu, Ningning ; Zhang, Jing ; Ning, Li
Author_Institution :
Dept. of Inf. Sci., Arkansas Univ., Little Rock, AR, USA
Abstract :
This paper considers the privacy-preserving cooperative linear system of equations (PPC-LSE) problem in a large, heterogeneous, distributed database scenario. It proposes a privacy-preserving algorithm to discover multivariate linear relationship based on factor analysis. Compared with other PPC-LSE algorithms, the proposed algorithm not only significantly reduces the communication cost, but also avoids the random matrix generation of either party to hide private information.
Keywords :
data mining; data privacy; distributed databases; security of data; very large databases; PPC-LSE; data mining; data security; distributed database; factor analysis; heterogeneous database; large database; multivariate linear relationship discovery; privacy-preserving cooperative linear system of equations; private information hiding; Algorithm design and analysis; Data mining; Diseases; Distributed databases; Equations; Information analysis; Linear systems; Partitioning algorithms; Protection; Vectors;
Conference_Titel :
Information Assurance Workshop, 2005. IAW '05. Proceedings from the Sixth Annual IEEE SMC
Print_ISBN :
0-7803-9290-6
DOI :
10.1109/IAW.2005.1495989
